How do you handle AI tools that make a judgment call vs. ones that just generate text?
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Generation tools are low-stakes when they're wrong draft an email, get a bad draft, edit it. Judgment tools are different: grading, flagging, scoring. If the output looks confident and it's actually a guess, someone might act on it without knowing that.
Working on letsflw's exam-checker made this concrete objective questions are easy (exact match, no ambiguity), but for open-ended answers we made a call to surface the AI's mark as a suggestion to review, not a final score, because presenting it as settled felt dishonest.
Curious how others building agent/AI tools are drawing that line different UI for "AI generated this" vs "AI judged this," or same treatment either way?
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For anything involving grading or evaluation, I always want a way to review the reasoning before acting on the results.
@saturnina_brigante That's the piece I keep coming back to a score alone doesn't tell you why, so you can't judge whether to trust it in this specific case. Right now the checker shows the rubric points it matched against, which is at least a step toward reasoning rather than just a number. Full step-by-step "here's my reasoning" is the harder version of this to get right without it turning into noise nobody reads.
@saturnina_brigante @farrukh_ahmed8877The outsourcing line is the one worth sitting with. Labeling something as suggested changes whether a person should double check it, but it doesn't create any record of whether they actually did. In a judgment call system, that gap, someone seeing an uncertainty flag versus someone genuinely engaging with it before acting, is exactly what an incident review needs answered later, and almost none of these tools capture it.
The rubric points you mentioned earlier are a good start, they at least give a human something concrete to check against. The harder problem is proving after the fact that the check actually happened instead of a click through. Most systems only think about the moment the flag gets shown, not what gets recorded afterward, so it doesn't show up anywhere until something goes wrong and someone asks why the flagged item still got approved.
someone i work with always says AI should assist with decisions, not replace them. I think that fits really well here.
@new_user___090202674ab6e030a7a9c52 That’s exactly what I meant. The real worry isn’t the AI getting it wrong it’s getting it wrong while looking just like it got it right. Assist-not-replace only works if you can actually tell the difference.
Funny how adding the word" suggested" changes how people interpret the exact same output. 😄
@luke_bell True, and it's a little uncomfortable how much that one word is doing. Same confidence, same model, same output the label is the only thing that changed, and it still shifts whether someone double-checks it or just runs with it. Makes me wonder how much of "trust" in these tools is really about the interface choices around the output rather than the output itself.
Trust isn't built by pretending AI is always right it's built by being honest about when it's making a judgment versus a suggestion.
@saira_bano4 That's the whole thing in one line, honestly. The moment a tool implies certainty it doesn't have, it's not really assisting anymore it's just outsourcing the risk to whoever trusted it. Being upfront about "this part is a guess" costs you a little confidence on the surface, but it's the only version that holds up once someone actually gets burned by trusting the wrong output.
Oscar Chat
Confidence should never be mistaken for certainty—that’s basically AI wearing a suit 😄 With Oscar Chat, we ground answers in approved business content and provide a path to a human when the available knowledge isn’t enough, rather than letting the AI confidently invent a decision: https://www.oscarchat.ai/ai-chatbot/